The project involves designing and maintaining data and solution architecture for banking systems, ensuring alignment with business strategy and IT standards. Responsibilities include managing architectural documentation, securing design approvals, and resolving operational issues in collaboration with DevOps and engineering teams. The focus is on enterprise data modeling, integration standards, and architectural governance. The project runs in an Agile environment.
Accountable for ensuring the products & services are supported by the right architectures, solutions and data models that meet customer needs
Accountable for ensuring the design of the product solutions and data models are cost effective and maintained through the agile development lifecycle, managing the flow of the backlog of design activities
Accountable for acquiring approvals from technical design and control authorities on solution and data designs
Working with DevOps Engineers to ensure operational issues (data / solution enhancement, performance, operator intervention, alerting, design defect related issues, etc) are resolved and that any design related issues are addressed in a timely manner
Work with Architects and DevOps to design and manage best practice on data and solution architecture with clear documentations
Work closely with the engineering teams and other stakeholders to meet the projects requirements aligned with Group standards, IT and business strategy
Identifying operational and delivery risks
Provide governance on architecture. Design, implement standards and target operating models for IT activities
Development and support of Integration standards, patterns, strategies, roadmaps and architectures
requirements-expected :
University graduate of Computer Science/IT-related field, preferably with major in Software/System engineering or relevant working experience in the field
At least 8 years’ data engineering / architecture; and 5 years’data modelling
Strong banking industry knowledge
Strong knowledge on data architecture, including data modeling (conceptual / logical / physical data modeling), transactional database solution designing vs analytical database solution designing, ETLs, data-related non-functional requirement design (e.g. backup, archiving, performance etc)
Data warehousing experience – Kimball, Inmon approaches for data modeling
Good technical background related to Big Data technologies & open source stack in Big data space:
